80 research outputs found
Towards a Framework to Manage Perceptual Uncertainty for Safe Automated Driving
Perception is a safety-critical function of autonomous vehicles and machine
learning (ML) plays a key role in its implementation. This position paper
identifies (1) perceptual uncertainty as a performance measure used to define
safety requirements and (2) its influence factors when using supervised ML.
This work is a first step towards a framework for measuring and controling the
effects of these factors and supplying evidence to support claims about
perceptual uncertainty
Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders
Convolutional autoencoders have emerged as popular methods for unsupervised
defect segmentation on image data. Most commonly, this task is performed by
thresholding a pixel-wise reconstruction error based on an distance.
This procedure, however, leads to large residuals whenever the reconstruction
encompasses slight localization inaccuracies around edges. It also fails to
reveal defective regions that have been visually altered when intensity values
stay roughly consistent. We show that these problems prevent these approaches
from being applied to complex real-world scenarios and that it cannot be easily
avoided by employing more elaborate architectures such as variational or
feature matching autoencoders. We propose to use a perceptual loss function
based on structural similarity which examines inter-dependencies between local
image regions, taking into account luminance, contrast and structural
information, instead of simply comparing single pixel values. It achieves
significant performance gains on a challenging real-world dataset of
nanofibrous materials and a novel dataset of two woven fabrics over the state
of the art approaches for unsupervised defect segmentation that use pixel-wise
reconstruction error metrics
GAN-based Hyperspectral Anomaly Detection
In this paper, we propose a generative adversarial network (GAN)-based
hyperspectral anomaly detection algorithm. In the proposed algorithm, we train
a GAN model to generate a synthetic background image which is close to the
original background image as much as possible. By subtracting the synthetic
image from the original one, we are able to remove the background from the
hyperspectral image. Anomaly detection is performed by applying Reed-Xiaoli
(RX) anomaly detector (AD) on the spectral difference image. In the
experimental part, we compare our proposed method with the classical RX,
Weighted-RX (WRX) and support vector data description (SVDD)-based anomaly
detectors and deep autoencoder anomaly detection (DAEAD) method on synthetic
and real hyperspectral images. The detection results show that our proposed
algorithm outperforms the other methods in the benchmark.Comment: 5 page
Active Authentication using an Autoencoder regularized CNN-based One-Class Classifier
Active authentication refers to the process in which users are unobtrusively
monitored and authenticated continuously throughout their interactions with
mobile devices. Generally, an active authentication problem is modelled as a
one class classification problem due to the unavailability of data from the
impostor users. Normally, the enrolled user is considered as the target class
(genuine) and the unauthorized users are considered as unknown classes
(impostor). We propose a convolutional neural network (CNN) based approach for
one class classification in which a zero centered Gaussian noise and an
autoencoder are used to model the pseudo-negative class and to regularize the
network to learn meaningful feature representations for one class data,
respectively. The overall network is trained using a combination of the
cross-entropy and the reconstruction error losses. A key feature of the
proposed approach is that any pre-trained CNN can be used as the base network
for one class classification. Effectiveness of the proposed framework is
demonstrated using three publically available face-based active authentication
datasets and it is shown that the proposed method achieves superior performance
compared to the traditional one class classification methods. The source code
is available at: github.com/otkupjnoz/oc-acnn.Comment: Accepted and to appear at AFGR 201
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